Leveraging Transfer Learning for Astronomical Image Analysis

TL;DR

This paper reviews how transfer learning with pre-trained neural networks enhances astronomical image analysis, enabling efficient detection and characterization of celestial phenomena amid data scarcity, especially for upcoming large surveys.

Contribution

It demonstrates recent applications of transfer learning in astronomy, highlighting its versatility and potential for future large-scale survey data analysis.

Findings

Transfer learning improves detection of active galactic nuclei.

It enables deriving galaxy parameters directly from images.

Effective in identifying artifacts and strong lensing candidates.

Abstract

The exponential growth of astronomical data from large-scale surveys has created both opportunities and challenges for the astrophysics community. This paper explores the possibilities offered by transfer learning techniques in addressing these challenges across various domains of astronomical research. We present a set of recent applications of transfer learning methods for astronomical tasks based on the usage of a pre-trained convolutional neural networks. The examples shortly discussed include the detection of candidate active galactic nuclei (AGN), the possibility of deriving physical parameters for galaxies directly from images, the identification of artifacts in time series images, and the detection of strong lensing candidates and outliers. We demonstrate how transfer learning enables efficient analysis of complex astronomical phenomena, particularly in scenarios where labeled data is scarce. This kind of method will be very helpful for upcoming large-scale surveys like the Rubin Legacy Survey of Space and Time (LSST). By showcasing successful implementations and discussing methodological approaches, we highlight the versatility and effectiveness of such techniques.